Spaces:
Running
Running
import os | |
import datasets | |
import pandas as pd | |
from datetime import datetime | |
from config import BACKUP_FOLDER, HF_DATASET_REPO_ID, HF_TOKEN, RESULTS_CSV_FILE, CSV_HEADERS | |
def main(): | |
""" | |
Gets the dataset from HF Hub where preferences are being collected, | |
save it locally to a backup folder with a timestamp. | |
Then creates an empty dataset with the same structure and saves it to the HF Hub. | |
""" | |
print(f"Attempting to load dataset '{HF_DATASET_REPO_ID}' from Hugging Face Hub (file: {RESULTS_CSV_FILE})...") | |
dataset = datasets.load_dataset(HF_DATASET_REPO_ID, data_files=RESULTS_CSV_FILE, token=HF_TOKEN, split='train') | |
print(f"Successfully loaded dataset. It has {len(dataset)} entries.") | |
dataset_df = dataset.to_pandas() | |
# 2. Save it locally to a backup folder with a timestamp | |
if not os.path.exists(BACKUP_FOLDER): | |
os.makedirs(BACKUP_FOLDER) | |
print(f"Created backup folder: {BACKUP_FOLDER}") | |
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") | |
backup_filename = f"preferences_backup_{timestamp}.csv" | |
backup_filepath = os.path.join(BACKUP_FOLDER, backup_filename) | |
try: | |
dataset_df.to_csv(backup_filepath, index=False) | |
print(f"Successfully backed up current preferences to: {backup_filepath}") | |
except Exception as e: | |
print(f"Error saving backup to {backup_filepath}: {e}") | |
if __name__ == "__main__": | |
main() |